When I first looked at my SEO data, everything seemed perfectly fine. All metrics from Google Search Console, traffic, and indexing were normal without any red flags. But then, I decided to dig deeper using Scrunch, our AI citation monitoring tool, to examine the platform presence for searchinfluence.com over the past 30 days.
Here’s what I found: Google AI Mode showed a presence of 37.8%, Copilot at 22.2%, Google Gemini at 16.3%, ChatGPT at 9.6%, and Perplexity at 7.8%. Alarmingly, both Claude and Meta AI were at 0.0%.
Two platforms had zero presence. Given that every crawler reads the same site, differences in content quality or topical authority couldn’t explain this discrepancy. The only factor that varied was crawler access.
To understand this further, I analyzed seven days of Cloudflare logs and discovered 29,099 bot requests, with 65.8% involving AI bots. The requests rate-limited with HTTP 429, or “too many requests,” were interestingly varied by bot user-agent.
Training crawlers that make bulk requests are throttled, while user-facing crawlers that mimic human pacing during live queries aren’t. For example, ClaudeBot made 20,583 crawl requests for each referral returned.
My assumption was that the 429 errors originated from Cloudflare, perhaps due to a web application firewall (WAF) or security plugin interference. I went down a rabbit hole investigating multiple layers. It was time-consuming and ultimately unnecessary.
The truth emerged when I performed a reproduction test using curl requests, revealing that the block was based on user-agent, not path or rate. The realization hit when I discovered the x-powered-by header: WP Engine hosted our site, and the block came from their platform infrastructure.
I then tested other AI bot UAs and crafted a fingerprint for each, discovering that the blocklist was outdated. While some bots were blocked, others like Common Crawl passed through unaffected.
In conclusion, while WP Engine’s firewall, documented on their support page, was intended as a security measure, it wasn’t transparent to customers. Identifying these blocks requires specific diagnostic steps, and the process taught me much about managed hosting’s hidden layers.
As I delved into the complexities of the AI search pipeline, I realized it’s a multiplicative system where even one weak link can constrain the overall results. I knew that understanding this could transform the visibility of my content.
The AI search pipeline consists of 10 crucial gates: Discovered, Selected, Crawled, Rendered, Indexed, Annotated, Recruited, Grounded, Displayed, and Won. Each gate is a critical checkpoint determining whether my content reaches its audience effectively.
If there’s a weakness at any of these gates, it can hinder the entire process, which reminded me of the “Straight C” principle: a system’s weakest link limits its potential. By focusing on fixing the weakest area first, I can leverage the most impactful improvements.
Brent D. Payne once highlighted this principle, and it stuck with me: “better to be a straight C student than three As and an F.” Identifying flaws and prioritizing them by impact ensures my content gets the attention it deserves.
Phase 1 of the pipeline (Discovery to Indexing) is mainly about infrastructure, while Phase 2 (Annotation to Winning) becomes competitive. My aim is to master both phases, ensuring my content passes smoothly through each gate.
I know that for some gates, the fixes are more straightforward, especially in Phase 1, where technical solutions are well-documented. In Phase 2, however, it becomes a battle of algorithmic performance, and differentiating my content means standing out against my competition.
Each stall at a gate indicates an area needing attention, and fixing these can vary greatly. It could be anything from enhancing server speed (for Crawled) to refining my entity signals for better Annotation.
By understanding where the bottlenecks are, I can strategically focus on improvements that elevate my content’s presence, making it more likely for AI systems to prefer my content over competitors’.
This approach becomes even more apparent when I dive into the details of entity optimization, understanding that if my brand’s entity is clear and confident, it greatly improves my content’s performance in downstream gates.
By optimizing my entity, I enhance clarity not just at a single gate, but across multiple, amplifying the benefits exponentially. As I prepare content, I want to audit what I already have, use what’s working, and expand strategically where necessary.
The realization that I should work from an outside-in approach revolutionized my content strategy. Instead of focusing purely on creation, I began valuing connecting existing proof with claims and framing them effectively.
The temporal triad—Return on Past Investment (ROPI), Return on Investment (ROI), and Return on Future Investment (ROFI)—guides my strategy. Before I create something new, I assess what can be leveraged from what I already have and plan strategically for the future.
Understanding this diagnostic framework, I could apply it universally across different AI engines, enhancing my content’s potential to be recommended, ensuring visibility and engagement.
I’ve discovered a fascinating truth about search in the age of AI: brand authority often outshines topical authority. The landscape of search has shifted, and it’s time for us to adapt.
While topical authority remains a beloved concept among SEO consultants pitching content, brand authority holds the reins in today’s AI-driven search landscape. Marketers have long discussed brand authority, though it was often dismissed or left to brand teams post-sitemap adjustments.
AI’s emergence has upended the traditional approach, revealing underlying issues. Search is crucial for the global economy, and the industry’s marketing approach needs re-examination. More content doesn’t automatically confer authority. In fact, AI search champions brands gaining notable visibility, mentions, and real demand.
Too many SEOs overlook the reasons people choose, trust, and remember brands. In this new world of AI search, such ignorance stands out even more. That’s why brand authority prevails—but not in the way our typical SEO tools might suggest.
Previously, the meaning of topical authority was intended to highlight genuine expertise through useful work, citations from others, and a growing associated reputation. This builds your brand’s association with a topic, which in turn, creates authority and fosters brand development.
However, the industry often marketed topical authority commercially, emphasizing volume over value. Technical SEO became a niche, links were outsourced or repackaged, but content was the consistent agency engine.
Pre-AI, this made sense. Creating good content involved rigorous processes and offered substantial value, earning rankings and supporting commercial interests. In contrast, topical authority introduced the misguided idea that mere keyword coverage equated to expertise, diluting the concept’s original intent.
Another intriguing aspect of authority is understanding what others say about you, rather than solely focusing on self-published content. Google’s Jun Wu highlighted the importance of ‘mention information’—how search engines discern topics, identify sources, and map relationships.
Our modern term for this is brand co-occurrence. Being consistently mentioned by authoritative sites and communities solidifies your brand’s association with a topic, elevating market perception and authority.
Many might pitch the concept of topical authority as building a comprehensive keyword strategy, but actual authority requires originating valuable data and sharing insights that engage audiences and capture media attention.
The changing economic landscape of AI means that traditional advertising methods through content must evolve. With AI offering direct answers, the value of certain traditional SEO practices is diminishing. Users, like my AI-liking father, prefer quick, synthesized information over cumbersome web browsing.
The rise of AI citations in search metrics has become a focus, but they differ from authentic human endorsements. Real influence is reflected through human testimonies, where your brand is discussed, cited, and recommended.
If measuring brand authority, brand searches present a clearer indicator of growth. If more people search specifically for your brand, it signals rising demand and market presence—a more accurate reflection of impact than solely relying on AI citations.
Traditional SEO still plays a role, ensuring you’re found where it matters—be it in search rankings or marketplaces. Yet, brand authority distinctly drives recommendations, and AI search is starting to favor consolidated options, often mentioning specific brands and solutions.
The future echoes the demand for meaningful engagement and widespread brand visibility. Though SEO isn’t dead, a simplistic keyword-centric approach is fading. A holistic approach integrating positioning, PR, reviews, and content as interconnected elements is pivotal.
In an era where fitness and visibility are equal determinants of success, brands must excel in products and services while ensuring their market presence is robust and omnipresent. After all, brand authority is what truly wins, confirming that mediocrity no longer warrants attention.
I recently found myself attempting to map out a Lumascape of answer engine optimization (AEO) tools. It’s a daunting task, and my computer simply doesn’t have the bandwidth for that!
Instead, I pivoted to focus on a select few tools I’ve been using effectively to boost my clients’ visibility in AI search results.
Here, I’m sharing a concise list: four tools that I consistently rely on, alongside three others I’m currently evaluating for potential integration into my workflow.
1. AI Assistants: ChatGPT, Claude, Perplexity
These AI assistants have proven invaluable. When used with intentionality, they serve as powerful tools for research and analysis in AEO.
For AEO, they assist in several key areas:
Competitive landscape research.
Content gap analysis.
Prompt testing.
Entity and topical coverage audits.
Structured content drafting.
The difference from casual usage lies in applying a specific AEO research methodology.
Why They’re Essential
Understanding AI systems processing is key to AEO, and regularly engaging with these tools analytically is the most direct way to gain that knowledge.
By querying AI with your audience’s prompts, you glean insightful data on sources, entities, and answer structures.
Competitive Strengths
These platforms each offer unique advantages:
ChatGPT is well-known for its broad synthesis of general knowledge.
Claude provides nuanced, analytical responses.
Perplexity excels with its clear citation methods, beneficial for AEO research.
What You Can’t Do Without Them
They are crucial for firsthand AEO status assessment, including:
Manual prompt testing: Assess your brand representation.
Competitive research: Use category-level queries to analyze competitor presentation.
Topical gap analysis: Identify missed opportunities.
Structural content analysis: Understand preferred AI answer formats.
Caveats
AI outputs are variable, influenced by many factors. These tools help build intuition and hypotheses that should be validated with quantitative data.
Beware of the time-consuming nature of manual testing. Establish a framework and stick to it.
2. Profound
Profound specializes in AEO intelligence, tracking how AI platforms interact with and cite your content. It also measures brand mention frequency, sentiment, and competitor visibility.
Why It’s Essential
Profound provides direct insights into your brand’s presence in the AI answer ecosystem, shifting the focus from rankings to visibility in AI responses.
Competitive Strengths
Its cross-platform view offers comparative insights, allowing you to see how your citation share compares to competitors.
What You Can’t Do Without It
Without it, quantifying your brand’s presence in AI-generated answers becomes difficult. It also tracks citation shares and identifies content driving AI mentions.
It’s a costly tool, but valuable for identifying areas where your brand is losing ground to competitors.
Caveats
As the tool evolves rapidly, the data remains a timely reflection of AI outputs. Remember, these metrics are signals, not precise rankings.
3. Google Trends and Google Keyword Planner
Google Trends shows search interest trends, while Keyword Planner gives search volume estimates, both critical for AEO strategy.
Why They’re Essential
Understanding demand is crucial for content optimization in AI answers. These tools provide reliable data on trending topics and search volume.
Competitive Strengths
While Google Trends offers momentum analysis, Keyword Planner’s forecasting can prioritize content based on future demand.
What You Can’t Do Without Them
Build a dynamic AEO strategy by monitoring demand trends and identifying emerging topics and seasonal patterns.
Caveats
These tools reflect traditional search behavior, not AI-acre queries, and Keyword Planner requires an active Google Ads account.
Always use them as a guide, not a complete picture, of AI demand.
4. Google Search Console and Google Analytics
These are essential for tracking search performance and on-site behavior, revealing insights into AI platform traffic and content effectiveness.
Why They’re Essential
They help diagnose whether AI-cited content is also visible in traditional search and track AI-driven visits and engagement.
Competitive Strengths
GSC offers unmatched query data, while GA4’s cross-channel tracking reveals AI platform engagement.
What You Can’t Do Without Them
Understanding AEO’s business impact and addressing indexing issues rely on these insights.
They illuminate high-impression, low-CTR content, indicating potential AI Overview cannibalization.
Caveats
GSC data is Google-centric and has some limitations, while GA4 requires precise configuration for accurate tracking.
Rapid-Fire Roundup
With numerous tools still to explore, consider testing these emerging options to assess their AEO value:
5. AI Trust Signals
This tool evaluates credibility signals influencing AI citation decisions. It’s a new dimension worth exploring as AI citation mechanics advance.
6. Ahrefs
Ahrefs shines with backlink analysis and content gap insights, indirectly supporting AEO by building authority signals.
Its Content Explorer helps identify high-performing content likely to be referenced by AI.
7. Roadway AI
This AI-native platform focuses on marketing growth activities, including attributing AEO signals to revenue.
Keep an eye on this developing option as it may gain importance quickly.
The Reality of AEO Tools: Fast-Moving and Imperfect
The AEO landscape is evolving, with tools still catching up. Prioritize consistent measurement, analysis, and testing to extract actionable insights.
Aiming for perfect setup may be unrealistic, but if a tool shows how it enhances your AEO efforts, that’s a positive start.
Consult industry colleagues with firsthand tool experience before committing, as better or cheaper alternatives may emerge soon.
I’ve seen how crucial it is to understand that AI visibility starts long before users hit that search bar and ends with citations.
These insights are vital in shaping what gets seen, summarized, and cited by AI systems.
Currently, the focus has shifted towards improving the AI ROI story, and I’m right in the thick of it, learning what strategies truly work.
This year, attending SMX Advanced will be more enlightening than ever, bringing unique perspectives and strategies.
Let’s dive into why influence matters everywhere, and how it impacts AI citations.
Rand Fishkin’s study, ‘Influence Happens Everywhere,’ reveals that, although Google commands the majority of search traffic, it’s the influence happening outside of search that truly dictates what people look for online.
For many, wandering through social media or news sites builds their understanding and interest long before the actual search occurs.
Despite the exciting growth of AI tools, achieving a stable presence online requires understanding how fragmented channels contribute to this influence.
When crafting content, it’s essential to dominate the influence phase so thoroughly that an AI assistant doesn’t just suggest your brand—it demands it.
That’s the strategic thrust behind the discussions at SMX Advanced in Boston and why I align my content calendar accordingly.
My colleagues at Search Engine Land are among those shaping these discussions. Insights from thought leaders like Dave Davies and Carolyn Shelby are invaluable.
They emphasize the importance of structured visibility signals and entity recognition, helping AI systems select the right brands to highlight.
In my own analysis, the various AI models like ChatGPT, Perplexity, and others have unique methodologies for selecting sources, reinforcing the idea that an engaged, multi-platform strategy is critical.
So, what does full-stack content truly mean today? It’s more than crafting blog posts; it’s about commanding entire topics with authority and depth, enhanced by AI tools like Jasper’s Enterprise Suite.
The ability to integrate real-time data, identify competitive content gaps, and create diverse multimedia content packages mean we’re shifting from simply generating content to dominating entire narratives.
But AI tools can only serve the overarching strategy if our content offers the original insights that help us stand out in AI retrieval systems.
This year, Purna Virji’s insights at SMX Advanced will challenge us to think critically about the real ROI in AI investment.
I’m particularly interested in seeing how Google Vids is democratizing video content by eliminating the high entry barriers of previous video production methods.
Now, video content can be produced and localized for a multitude of markets rapidly, a paradigm shift in how we engage audiences across the globe.
The standards AI is setting for content — whether text, video, or multimedia — require a strategic framework that aligns with evolving platforms like GEO and AEO.
For those in the trenches like me, adjusting focus towards an integration of structured data and earned media becomes imperative.
The real challenge isn’t in the buzzwords but effectively navigating the volatile landscape of AI-driven citations.
I recognize the adjustments needed in approach, especially when considering the stark differences in referral and conversion rates from traditional search versus AI platforms.
So, practical actions for the rest of 2026? Audit your AI presence thoroughly, stop gating original research, secure your place in vibrant communities, and refine your focus towards citatability rather than simple visibility.
Ultimately, the brands ready to adapt will continue to thrive in this AI-enhanced environment.
Indeed, the bots are crawling, and it’s time I ensured my brand is worth citing.
I’ve been on a journey to develop over 10 SEO agent skills in just 34 days. Six of these succeeded on the first attempt, while the remaining four taught me invaluable lessons, especially about the overlooked importance of folder structure that many LinkedIn posts on AI SEO skills seem to miss.
The reliability of these agents isn’t about crafting superior prompts; it lies in the architecture that supports them. Here’s my blueprint for building an agent from scratch, testing it diligently, refining it, and deploying it with full confidence.
Here’s why many AI SEO skills don’t make the cut.
A typical AI SEO prompt seen on platforms like LinkedIn usually looks something like this:
You are an SEO expert. Analyze the following website and provide a comprehensive audit with recommendations.
And that’s where it ends. One simple prompt, often coupled with some formatting directions, is shared with the world. The post then earns hundreds of likes, yet the output—while polished—is often up to 40% inaccurate.
I know because I’ve been there. Initially, I tasked an agent to identify SEO issues on a website, and while it came back with 20 findings, eight were non-existent. The agent hadn’t truly visited many of the reported URLs.
Here are three key issues that doom single-prompt skills:
No tools: The agent can’t physically verify the website; it relies on training data to guess. Queries about canonical tags, for instance, result in assumptions rather than real-time analysis of HTML.
No verification: There’s no check on the truthfulness of output. An agent might report missing meta descriptions across 15 pages, but without verification, we don’t know if these pages are even indexed correctly or intentionally set as noindexed.
No memory: The agent’s feedback varies wildly with each use, showing inconsistency due to the lack of a template or structured history of previous runs.
In essence, if your skill is just a prompt within a lone file, you’ve got a 50/50 chance at best.
Every agent in my system has a dedicated workspace. Consider it akin to a new employee’s desk, equipped with all necessary resources. For example, our agent designed to crawl and map website architecture works within this kind of structured environment:
agent-workspace/
AGENTS.md instructions, rules, output format
SOUL.md personality, principles, quality bar
scripts/
crawl_site.js tool the agent calls to crawl
parse_sitemap.sh tool to read XML sitemaps
references/
criteria.md what counts as an issue vs noise
gotchas.md known false positives to watch for
memory/
runs.log past execution history
templates/
output.md expected output structure
The workspace includes six key components services that just one prompt couldn’t dream of covering fully.
Within AGENTS.md, I’ve articulated a meticulous methodology comprising thousands of words. Instead of a simple instruction like “crawl the site,” I detailed each step: “Start with the sitemap; if it doesn’t exist, check various routes like /sitemap.xml, /sitemap_index.xml, and robots.txt for references.”
Scripts represent the tools the agent utilizes. Instead of writing curl commands from scratch for each crawl, the agent can run node crawl_site.js -url to analyze website data, which is far more efficient and reliable.
References consist of criteria that help the agent distinguish between significant issues and noisy false positives, using a wealth of knowledge I’ve amassed over two decades.
To ensure that every execution is informed by the past, I keep meticulous logs under memory, serving as institutional knowledge that empowers consistency across agent runs.
Through templates, I outline the exact format I expect from the output, thereby maintaining high quality across multiple iterations of the same task.
Building from scratch, the first naive attempt involved simple instructions that inevitably failed when confronted with modern CDNs. By iterating and incorporating tools like crawl_site.js, enhancing with rate limiting, and tackling JavaScript rendering, I’ve honed an architecture that delivers consistent outputs across runs.
The path involves a series of iterations where each failure metamorphoses into a permanent lesson, gradually shaping a sophisticated system. This methodically structured approach ensures that what we build is not just technically proficient but measurably better with every successive run.
As I dive into the world of Programmatic SEO (pSEO), I understand that many people in the industry view it with suspicion, associating it with low-quality pages and duplication. Often, it’s seen simply as replicating city names on static templates.
Google’s policies on content spam are clear: strategies that generate unoriginal content just to influence rankings will not be tolerated.
In the modern landscape, pSEO isn’t about mass page generation. Instead, I aim to address thousands of search intents with local specificity and semantic depth, achieving what isn’t possible manually.
Here, I share my blueprint for transitioning from syntax-based to semantics-based pSEO, using methods we’ve tested with major companies in Brazil.
When embarking on a pSEO project, it’s common to start with templates. Yet, this approach often misses the mark. For instance, the intent behind “Best Hotel in [Las Vegas]” differs from “Best Hotel in [Orlando],” focusing on entirely different priorities and amenities.
I leverage AI to make content more granular, ensuring that each page addresses unique travel intents rather than generic keywords. My goal isn’t just to create a thousand pages, but a thousand pages that each fulfill a specific travel need.
Before creating content, I must answer a vital question: where does my domain have authority to rank? Failed pSEO projects often miss this step, targeting areas without established authority. My solution involves deep analysis using real Google Search Console data.
Through cluster audits, priority definitions, and strategic calendar alignment, I ensure my pSEO actions enhance topical authority while addressing existing semantic gaps.
Brand consistency is a hurdle when adopting AI. By implementing context governance, I ensure AI-generated content remains true to the brand’s voice, using guidelines to prevent deviations.
For internal linking, I adopt the semantic mesh strategy to ensure that every page connects logically, directing the user through a logical journey rather than dead ends.
In practice, understanding regionalization and seasonality at scale is crucial. Ânima Educação in Brazil is a perfect case study, showing how strategic pSEO leads to precision and considerable business impact.
As I scale content, monitoring with technical SEO agents helps maintain site quality, foreseeing issues like indexing problems or high LCP in real time.
In summary, successful SEO is about integrating the efficiency of technology with the nuanced human touch to deliver timely and relevant content to users.
I’ve been diving into the world of ChatGPT lately, and it turns out there’s a fascinating bias toward commercial intent in its fan-out analysis. Let me break down what this means for our content strategy.
Over the course of my tests with 90 ChatGPT prompts, I discovered that commercial prompts lead to web searches a whopping 78.3% of the time, while informational prompts only did so 3.1% of the time.
This discrepancy got me thinking about how to craft our content to increase the chances of being featured in ChatGPT responses.
ChatGPT doesn’t source every response from the same place. Some responses are derived from its training data, while others are based on live web searches. This process, known as query fan-out, involves expanding a prompt into several background searches, and then curating a synthesized response from multiple topics. If our pages don’t fit into these subtopics, we miss out on getting pulled in.
So, our challenge now is more than just ranking well; it’s about ensuring our pages open the door to this fan-out process from the get-go.
In our samples, informational pages fell short. I encourage you to continue reading to uncover the paths the system actually favored.
I conducted tests across three industries: beauty, legaltech/regtech, and IT. The analysis explored prompt intents, the resulting query expansions, and the intents portrayed by those expansions.
The main takeaway is that most queries are aligned with commercial, rather than purely informational, intent.
Why This Question Matters Now and the Role of Query Fan-Outs
Understanding query fan-outs is crucial because it shifts the content creation approach. The system expands a prompt into several background searches, retrieving and synthesizing information from these subtopics.
This behavior triggers parallel web searches connected to the initial prompt, providing opportunities for mentions and citations.
Multi-query expansion is a fundamental design element in today’s search systems. As Google describes AI Mode, it breaks a question into subtopics, searches them simultaneously across sources, and combines the results into one coherent response.
This raises a key strategic question: should we focus more on top-of-funnel educational content or on lower-funnel comparison, shortlist, and recommendation content?
I designed this experiment to address that problem.
We aimed to see where fan-out occurs by intent category across selected industries: informational, commercial, transactional, or branded.
The hypothesis was clear: while informational prompts wouldn’t trigger fan-out, commercial ones would, and would either remain at the same level or move further down the funnel.
ChatGPT fan-outs were observed to align predominantly with commercial intent.
Disclaimer: This analysis reflects observed prompt expansion behavior in ChatGPT. Although Google AI Mode is cited for context to illustrate multi-query expansion as a pattern, it is not evidence of ChatGPT’s architecture.
The Setup: What We Tested
The experiment sampled 90 prompts, focusing heavily on informational intent.
Prompt intent
Prompts
Share of sample
Prompts with fan-out
Fan-out rate
Informational
65
72.2%
2
3.1%
Commercial
23
25.6%
18
78.3%
Branded
1
1.1%
0
0.0%
Transactional
1
1.1%
0
0.0%
Our sample primarily featured informational prompts, with some commercial and very few branded and transactional ones.
The test was structured around three industries: beauty/personal care, legaltech/regtech, and IT/tech.
The Result: Commercial Prompts Dominated
The findings were clear and conclusive.
Of the 90 prompts, 20 triggered a fan-out. Remarkably, 18 were of commercial intent and only 2 were informational.
Informational prompts accounted for a mere 10% of fan-out triggers (2 out of 20). When they triggered expansion, they were reframed into more evaluative, solution-seeking subqueries.
This indicates that, in this sample, commercial intent overwhelmingly influenced fan-out.
These 20 prompts resulted in 42 fan-out queries, averaging 2.1 per triggered prompt.
Here’s a breakdown of those fan-out queries:
39 were commercial.
2 were branded.
1 was informational.
Even if a prompt led to expansion, it typically transformed into a focus on comparison, feature filtering, shortlist creation, or brand-specific exploration, not broad educational discovery.
Methodology: Our Analytical Approach
Our experiment involved 90 prompts across three industries, mainly informational with fewer commercial prompts, and minimal branded and transactional queries.
The analysis involved:
Choosing a representative set of prompts.
Identifying fan-outs.
Classifying each fan-out by intent.
Analyzing distribution by prompt metadata.
Our approach followed three key steps:
Classifying prompts by intent labels.
Counting prompts that triggered any fan-out.
Reviewing expansion queries and their intent labels.
This process revealed two distinct perspectives:
A prompt-level view to determine which prompts instigated fan-out.
A fan-out-query view to assess the intent of downstream expansions.
This distinction is important: the first identifies prompts that initiate the fan-out path, while the second examines where the system proceeds once engaged.
Interpreting the Results: Fan-Outs Trend Down-Funnel
The clearest takeaway is that, in this instance, fan-outs behave more like decision support rather than topic exploration.
Commercial prompts frequently opened new discovery paths.
Once open, these paths typically remained commercially focused.
The system often expanded into comparisons, feature-based analyses, product listings, and pricing inquiries.
Here are some illustrative examples:
“Suggest the best accounting software for small business and explain why” expanded to a commercial comparison query on features.
“What are the top AI document management systems for lawyers?” led to multiple product-centered legaltech queries.
“What are the best products for skin care?” grew into a shortlist-style inquiry around product categories and reviews.
The rare informational examples expose more about the system’s tendencies than the rules themselves.
“I need an open-source document management system. What can you suggest?” initially coded informational, shifted to solution recommendations.
“AI tools for legal research and document automation” also redirected into clearly commercial/evaluative queries.
Ultimately, even broad prompts frequently translate into more focused, commercially driven retrieval paths.
Implications for Our Content Strategy
Let’s not abandon informational content; however, we should recognize that informational content alone doesn’t consistently align with fan-out expansions, at least in this dataset.
If our goal is to shine in AI responses tied to product selection or vendor discovery, we need to strengthen our coverage with content that lines up with these downstream commercial intents.
Consider the following:
Creating “best-of” and shortlist pages
Developing thorough comparison pages
Writing “which tool should I choose” guides
Feature-led category explainers
Alternative option pages
Evaluation-focused FAQs
Incorporating recommendation passages in broader educational pieces
In practical terms, our content model should integrate both top- and bottom-of-funnel strategies, with strong commercial bridges.
A comprehensive piece can still be beneficial, provided it contains elements that the system can readily transform into decision-support inquiries.
An educational piece that lacks direct references to products, tradeoffs, features, use cases, or selection criteria is less likely to match the system’s fan-out paths.
In short, consider not only answering the obvious inquiries but also forecasting the subsequent evaluative step the system might generate behind the scenes.
Understanding Our Limitations
These results offer direction rather than universal truths.
90 prompts highlight a pattern, but don’t establish AI retrieval behavior as a law.
The prompt mix skews heavily towards informational content, with few branded or transactional samples. The findings don’t signify absence.
While diverse, the dataset isn’t normalized for brand, style, or use case. Some sectors lean easily into product-discovery language.
This analysis observed recorded fan-outs rather than controlling for platform-level testing. It reflects what occurred within this set rather than guarantees of ChatGPT’s constant behavior.
Google’s fan-out description provides context; however, this isn’t a Google AI Mode test. It’s ChatGPT-centric, with strategic—not architectural—takeaways.
Next Steps for Testing
Future versions of this test should further isolate the question while widening the dataset.
A follow-up should map fan-outs to specific content formats.
The aim isn’t solely to affirm that commercial intent triumphs, but to pinpoint which page templates and structures proficiently capture AI-preferred fan-out paths.
For over two decades, I’ve witnessed backlinks as foundational to SEO. Google’s PageRank revolutionized search by using backlinks as proxies for trust.
Backlinks were more than just pathways; they were votes of confidence. The more votes you gathered from authoritative sources, the better your rankings soared.
But times have changed. As Google advanced, AI systems evolved, and the necessity for hyperlinks diminished as entity-based understanding gained ground.
Today, visibility isn’t solely dependent on links. It’s amplified by the broad range of signals signifying your brand’s mentions, citations, and trust across well-regarded platforms.
This shift sees search engines and AI prioritize these overarching signals.
AI’s Role in Evolving SEO
Modern AI models assess trust and expertise in unprecedented ways. They’ve reshaped authority, focusing less on backlinks and more on diverse digital signals.
AI can now:
Identify and relate entities online.
Interpret sentiment and context.
Spot artificial link patterns.
Gauge brand prominence sans hyperlinks.
Evaluate reputation from reviews and citations.
Integrate information across varying sources.
Mentions in respected publications, even link-free, enhance entity authority. Consistent expert citations affirm expertise. These are the signals forging a new era where authority becomes a rich network.
The Shift to Entity-First SEO
With Google’s move away from pure link signals, the notion of entities—people, brands, concepts—gains importance. Google elevates brands based on identity and conversation rather than just their backlink profile.
In essence, entity-first SEO involves mapping and understanding brand interactions and references across trusted sources.
An example: An outdoor brand with a modest backlink profile gained visibility in AI Overviews for “best hiking backpacks” due to mentions in Reddit discussions and YouTube reviews, illustrating real-world relevance sans hyperlinks.
If your brand consistently figures positively in related talks, it’s seen as relevant and trusted—characteristics essential for success.
Volume-focused link building loses ground as AI discerns unnatural patterns. Quality-driven, relevant links, coupled with PR signals, grow increasingly essential.
Editorial PR links from credible sources signal genuine credibility, like a trusted expert affirming a brand’s significance.
AI not only checks link presence but evaluates surrounding context, striving to reward the most authoritative entities.
Building Multi-Signal Authority
The potency of multi-signal authority lies in blending various signals. As the digital landscape evolves, quality shines over quantity.
AI prompts this evolution by advancing traditional, relevance-based links alongside diversified brand signals.
Strategic placements can yield:
Brand mentions affirming presence.
Citations validating expertise.
Positive sentiment enhancing trust.
Topical relevance and growth-enabling links.
Boosted Knowledge Graph associations.
Secondary coverage spreading influence.
Multi-signal authority offers AI the understanding that your brand is recognized, trusted, and worth conversation.
PR signals, albeit crucial, are but a fragment of the comprehensive authority ecosystem AI evaluates.
Decoding the New Authority Framework
Today, authority hinges on varied and consistent validation signals, akin to human assessment—through reputation and recognition.
It’s no longer just links. Authority encompasses:
Brand strength: Upward branded search and direct traffic echo real-world recognition.
Reputation signals: Trust reflected in reviews, citations, sentiments.
PR signals: News, interviews, industry mentions bolster relevance.
These interwoven signals forge a comprehensive authority profile, which AI recognizes. The dominating brands have the most impactful multi-signal authority footprint.
Brand Strength’s Quiet Influence
Brand strength silently prevails over other signals. Data reveals brands ranking in the top 25% for web mentions average far higher AI Overview citations than their counterparts.
This aligns with Ahrefs’ analysis of ~75,000 brands, underscoring branded web mentions and search volume as indicators of genuine brand presence.
Consider two fitness apps: one with extensive generic backlinks, another actively part of social and media conversations. The latter’s real-world engagement ensures consistent AI Overview visibility.
Leading brands in AI Overviews have robust brand presence supported by consistent links, mentions, and relevance.
Future Predictions for 2027 and Beyond
By 2027, link building evolves from a numbers focus to a confidence-driven model with new metrics like Share of Authority.
Here are my predictions:
Prediction 1: Visibility via “Share of Model” Metric
Strategies will shift towards “seeding” information in places AI relies on, moving away from mass low-tier blog outreach to user-chosen platforms like Reddit, which AI values.
Brands frequently appearing in AI training data will gain visibility, defining the new authority landscape.
Prediction 2: Brands as Primary News Sources
In AI-led ecosystems, proprietary data will emerge as critical, offering natural, highly trusted authority signals.
Data evolves from mere content to a powerful signal engine, enriching PR coverage, citations, and discussions.
Traditional link building remains vital, but data-driven assets are vital accelerants.
Prediction 3: Rising Value of Unlinked Mentions
While foundational, traditional links will gain strength from semantic context and relate directly to brand mentions enhancing entity strength.
Exploring AI’s Expanding Role in SEO
The off-page SEO future merges traditional link building with AI-driven signals recognizing links as just one part of a broader array AI processes.
Both remain essential: links for foundational relevance, AI for context, sentiment, and entity evaluation.
Links are the foundation. Signals construct the skyscraper.
SEO is evolving, but it’s certainly not disappearing. In my journey through the changing landscape, I’ve found that blending traditional SEO techniques with emerging AI search practices is crucial for staying ahead.
SEO is at a fascinating juncture. On one side, there’s a push to optimize for AI and large language models (LLMs), while on the other, some want to stick to the tried-and-true methods. I’ve found a middle path — merging core SEO principles with an awareness of LLMs and their operations.
Embracing this approach means holding onto effective strategies like on-page SEO and quality backlinks while also exploring new avenues such as optimizing for query fan-out and new prompt intents. Since the rise of tools like ChatGPT, my research has focused on how AI engines present search results and the future direction of SEO.
Here’s what I’ve learned and how you can adjust your strategy to consider human behavior at the forefront of SEO innovations.
How the Red Queen theory applies to AI search
The Red Queen evolutionary model suggests that we must constantly adapt to maintain our position; if we don’t evolve, we risk falling behind. This is exactly the case in the world of AI and SEO — stand still, and you’ll be left behind.
As you and your competitors adapt, you must maintain your competitive edge. In SEO, failing to adapt means losing visibility and influence.
How to apply the Red Queen principle to your AI SEO strategy
The evolution of AI search is a continuation of developments over the past decade. With concepts like RankBrain since 2015, familiar SEO tactics remain relevant. This isn’t about a complete overhaul but rather a series of adaptations and improvements.
Core elements like retrieval-based search engines, content quality, speed, and intent matching are as important as ever. By focusing on these, alongside optimizing for AI retrieval and third-party visibility, you position yourself favorably.
Why RAG is essential to understanding AI search
One effective way I’ve discovered to engage with AI search is by understanding its limitations, particularly their reliance on retrieval-augmented generation (RAG) systems. RAG helps fill the gaps in LLM databases without constant updates, ensuring relevant answers are provided.
In practice, this involves seeing how AI tools like Google AI Mode and ChatGPT respond to prompts and identifying where they draw their information. Using this insight, you can ensure your content is part of the external sources AI assists rely upon.
How to optimize for AI search vs. traditional search
Understanding how your content interacts with AI engines’ limitations is critical. AI does its own searching and then provides answers, sometimes without showcasing external sources. Therefore, becoming a trusted source for LLMs is the key to SEO in the AI era.
It’s essential to analyze AI answers, understand their behavior, and continuously evaluate their preferences. By feeding these systems with quality data, we can ensure we’re among the go-to trusted sources AI assistants reference.
The long-term future of SEO relies on human behavior
Long-term SEO strategies should remain focused on understanding human behavior. This involves pinpointing search intent and analyzing how AI-generated queries align with different user needs and intents.
Being successful means considering both traditional search intents and new AI-induced intents to provide valuable content that resonates with user needs. It’s about dynamically adapting approaches based on observed behavior and striving to stay ahead in this ever-evolving field.
